Alameddine Abdallah K, Conlin Frederick, Binnall Brian
Division of Cardiac Surgery, Baystate Medical Center, Springfield, MA, USA.
Department of Anesthesiology, Baystate Medical Center, Springfield, MA, USA.
Cancer Inform. 2018 Sep 12;17:1176935118799754. doi: 10.1177/1176935118799754. eCollection 2018.
Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention.
We introduce simplified mathematical tools to model serial quantitative data of cancer biomarkers. We also highlight an introductory overview of mathematical tools and models as they apply from the viewpoint of known cancer features.
Mathematical modeling of potentially actionable genomic products and how they proceed overtime during tumorigenesis are explored. This report is intended to be instinctive without being overly technical.
To date, many mathematical models of the common features of cancer have been developed. However, the dynamic of integrated heterogeneous processes and their cross talks related to carcinogenesis remains to be resolved.
In cancer research, outlining mathematical modeling of experimentally obtained data snapshots of molecular species may provide insights into a better understanding of the multiple biochemical circuits. Recent discoveries have provided support for the existence of complex cancer progression in dynamics that span from a simple 1-dimensional deterministic system to a stochastic (ie, probabilistic) or to an oscillatory and multistable networks. Further research in mathematical modeling of cancer progression, based on the evolving molecular kinetics (time series), could inform a specific and a predictive behavior about the global systems biology of vulnerable tumor cells in their earlier stages of oncogenesis. On this footing, new preventive measures and anticancer therapy could then be constructed.
癌症中经常出现调控肿瘤代谢物的异常改变、各种癌基因/表观遗传随机性、抑癌基因，以及错配修复机制缺陷、慢性炎症或其他属于癌症特征的偏差。这些随时间演变的异常如何决定恶性肿瘤的整体表型仍有待完全理解。动态分析可能有潜力揭示致癌机制，并能提供新的治疗干预措施。
我们引入简化的数学工具来对癌症生物标志物的系列定量数据进行建模。我们还从已知癌症特征的角度重点介绍数学工具和模型的入门概述。
探索潜在可操作的基因组产物的数学建模以及它们在肿瘤发生过程中随时间的变化情况。本报告旨在直观易懂，不过度技术化。
迄今为止，已经开发了许多关于癌症常见特征的数学模型。然而，与致癌作用相关的综合异质过程的动态及其相互作用仍有待解决。
在癌症研究中，概述实验获得的分子物种数据快照的数学建模可能有助于更好地理解多个生化回路。最近的发现支持了复杂癌症进展的存在，其动态范围从简单的一维确定性系统到随机（即概率性）或振荡和多稳态网络。基于不断演变的分子动力学（时间序列）对癌症进展进行进一步的数学建模研究，可以为脆弱肿瘤细胞在肿瘤发生早期阶段的全球系统生物学提供特定的预测行为。在此基础上，可以构建新的预防措施和抗癌疗法。
